How do you handle missing and corrupted data in datasets?
Reviewed by Jason Burns, Editorial Steward · Last updated
Handle missing and corrupted data by first understanding why it is missing, then applying the appropriate fix: drop rows, impute with a statistical or model-based estimate, use algorithms that natively handle missingness, or flag the missing-ness as its own feature.